This resubmitted proposal for a Mentored Quantitative Research Career Development Award will provide John Veranth with funding to combine his combustion and aerosol engineering background with cell biochemistry research under the supervision of Dr. Garold Yost as the sponsor and Dr. Ann Aust as the co-sponsor. The research is motivated by the goal of applying mass transfer and heterogeneous (solid-liquid) reaction analysis techniques from chemical engineering to the study of the interactions of low-solubility inorganic air pollution particles with lung cells. The candidate's research emphasis will be on quantitative analysis of intracellular iron dose as a function of measurable particle characteristics and the correlation of this dose with cytokine signaling responses. Results from computational simulations will be compared with experimental data obtained in cultured cell lines, in fresh lung macrophages, and from whole animal inhalation studies. The study is motivated by the biological hypothesis that ambient particles can deliver an inappropriate dose of redox active transition metals to lung tissues where the metals catalyze the formation of reactive oxygen species, initiating a cascade of cytokine signaling responses. Further, these cytokine signals are proposed as a mechanistic link between air pollution and certain adverse effects in sensitive individuals. The specific aims are: aim 1: Develop, using current literature data, a computational model that predicts the intracellular dose of iron or other transition metals in target cells and airway tissues based on measurable particle characteristics. Specific aim 2: Determine the effect of temperature, oxidation, and moisture history on the ability of inorganic particles to release redox-active metals under physiological conditions. Specific aim 3: Elucidate the kinetics of key mechanistic steps of particle-induced proinflammatory responses in appropriate lung cells by measuring intracellular iron concentration and selected signaling responses. Specific aim 4: Use data obtained from cell culture and whole animal inhalation studies to refine and improve the computational model.